


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     DiTPipeline
       _lowercase											=     CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       _lowercase											=     PipelineTesterMixin.required_optional_params - {
           """latents""",
           """num_images_per_prompt""",
           """callback""",
           """callback_steps""",
       }
       _lowercase											=     CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       _lowercase											=     False







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			TransformeraDModel(
                sample_size=16,num_layers=2,patch_size=4,attention_head_dim=8,num_attention_heads=2,in_channels=4,out_channels=8,attention_bias=A_,activation_fn='gelu-approximate',num_embeds_ada_norm=1000,norm_type='ada_norm_zero',norm_elementwise_affine=A_,)
            __UpperCamelCase =			AutoencoderKL()
            __UpperCamelCase =			DDIMScheduler()
            __UpperCamelCase =			{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
            return components







       def 		snake_case_	(			self: Optional[Any],A_: Any,A_: int=0					):






            '''simple docstring'''




            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'class_labels': [1],
                'generator': generator,
                'num_inference_steps': 2,
                'output_type': 'numpy',
            }
            return inputs







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'

            __UpperCamelCase =			self.get_dummy_components()
            __UpperCamelCase =			self.pipeline_class(**A_					)
            pipe.to(A_					)
            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			self.get_dummy_inputs(A_					)
            __UpperCamelCase =			pipe(**A_					).images
            __UpperCamelCase =			image[0, -3:, -3:, -1]

            self.assertEqual(image.shape,(1, 16, 16, 3)					)
            __UpperCamelCase =			np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]					)
            __UpperCamelCase =			np.abs(image_slice.flatten() - expected_slice					).max()
            self.assertLessEqual(A_,1E-3					)







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            self._test_inference_batch_single_identical(relax_max_difference=A_,expected_max_diff=1E-3					)







       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available(),reason='XFormers attention is only available with CUDA and `xformers` installed',)
       def 		snake_case_	(			self: List[Any]					):






            '''simple docstring'''




            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



@require_torch_gpu
@slow
class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			torch.manual_seed(0					)

            __UpperCamelCase =			DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'					)
            pipe.to('cuda'					)

            __UpperCamelCase =			['vase', 'umbrella', 'white shark', 'white wolf']
            __UpperCamelCase =			pipe.get_label_ids(A_					)

            __UpperCamelCase =			pipe(A_,generator=A_,num_inference_steps=40,output_type='np'					).images

            for word, image in zip(A_,A_					):
                 __UpperCamelCase =			load_numpy(
                     F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''					)
                 assert np.abs((expected_image - image).max()					) < 1E-2







       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            __UpperCamelCase =			DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'					)
            __UpperCamelCase =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
            pipe.to('cuda'					)

            __UpperCamelCase =			['vase', 'umbrella']
            __UpperCamelCase =			pipe.get_label_ids(A_					)

            __UpperCamelCase =			torch.manual_seed(0					)
            __UpperCamelCase =			pipe(A_,generator=A_,num_inference_steps=25,output_type='np'					).images

            for word, image in zip(A_,A_					):
                 __UpperCamelCase =			load_numpy(
                     'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                     F'''/dit/{word}_512.npy'''					)

                 assert np.abs((expected_image - image).max()					) < 1E-1



def 			lowercase(	UpperCamelCase_ = 100       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      n * (n + 1) * (2 * n + 1) / 6
 UpperCamelCase			  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares       )


if __name__ == "__main__":
     print(F'''{solution() = }''')
